Google Symptoms Dataset
Addison
POSTAL_TO_STATE = list('AL'='Alabama', 'AK'='Alaska', 'AS'='American Samoa',
'AZ'='Arizona', 'AR'='Arkansas', 'CA'='California',
'CO'='Colorado', 'CT'='Connecticut', 'DE'='Delaware',
'DC'='District of Columbia', 'FL'='Florida',
'GA'='Georgia', 'GU'='Guam', 'HI'='Hawaii',
'ID'='Idaho', 'IL'='Illinois', 'IN'='Indiana',
'IA'='Iowa', 'KS'='Kansas', 'KY'='Kentucky',
'LA'='Louisiana', 'ME'='Maine', 'MD'='Maryland',
'MA'='Massachusetts', 'MI'='Michigan', 'MN'='Minnesota',
'MS'='Mississippi', 'MO'='Missouri', 'MT'='Montana',
'NE'='Nebraska', 'NV'='Nevada', 'NH'='New Hampshire',
'NJ'='New Jersey', 'NM'='New Mexico', 'NY'='New York',
'NC'='North Carolina', 'ND'='North Dakota',
'MP'='Northern Mariana Islands', 'OH'='Ohio',
'OK'='Oklahoma', 'OR'='Oregon', 'PA'='Pennsylvania',
'PR'='Puerto Rico', 'RI'='Rhode Island', 'SC'='South Carolina',
'SD'='South Dakota', 'TN'='Tennessee',
'TX'='Texas', 'UT'='Utah', 'VT'='Vermont', 'VI'='Virgin Islands',
'VA'='Virginia', 'WA'='Washington', 'WV'='West Virginia',
'WI'='Wisconsin', 'WY'='Wyoming')
states = c("al", "ak", "az", "ar", "ca", "co", "ct", "de", "fl", "ga", "hi",
"id", "il", "in", "ia", "ks", "ky", "la", "me", "md", "ma", "mi",
"mn", "ms", "mo", "mt", "ne", "nv", "nh", "nj", "nm", "ny", "nc",
"nd", "oh", "ok", "or", "pa", "ri", "sc", "sd", "tn", "tx", "ut",
"vt", "va", "wa", "wv", "wi", "wy")
BASE_DAILY_URL = paste0(
'https://raw.githubusercontent.com/google-research/open-covid-19-data/',
'master/data/exports/search_trends_symptoms_dataset/',
'United%20States%20of%20America/subregions/{state}/',
'2020_US_{state_underscore}_daily_symptoms_dataset.csv')
cache_data_list = list()
signal_description_df = tribble(
~signal, ~description,
'Podalgia', 'pain in the foot',
'Anosmia', 'loss of smell',
'Purpura', "red/purple skin spots; 'blood spots'",
'Radiculopathy', 'pinched nerve',
'Ageusia', 'loss of taste',
'Erythema chronicum migrans', 'expanding rash early in lyme disease',
'Photodermatitis', 'allergic rash that reqs light',
)expand_state_name = function(state) {
state_name = POSTAL_TO_STATE[[str_to_upper(state)]]
return(state_name)
}
load_state_data = function(state) {
if (state %in% names(cache_data_list)) return (cache_data_list[[state]])
# Check whether there is a cached version
state_fname = sprintf('cache/%s.csv', state)
# if there isn't, then download
if (!file.exists(state_fname)) {
state_name = expand_state_name(state)
message(sprintf('Downloading data for %s...', state_name))
state_name_underscore = str_replace_all(state_name, ' ', '_')
STATE_DAILY_URL = str_replace_all(BASE_DAILY_URL,
fixed('{state}'), state_name)
STATE_DAILY_URL = str_replace_all(STATE_DAILY_URL,
fixed('{state_underscore}'),
state_name_underscore)
STATE_DAILY_URL = str_replace_all(STATE_DAILY_URL,
fixed(' '),
'%20')
download.file(STATE_DAILY_URL, state_fname)
}
single_state = readr::read_csv(state_fname)
cache_data_list[[state]] <<- single_state
return (single_state)
}
pull_data_state = function(state, symptom) {
single_state = load_state_data(state)
unique(single_state$sub_region_2_code)
single_state_counties = single_state[!is.na(single_state$sub_region_2_code),]
selected_symptom = paste0('symptom:', symptom)
single_state_symptom = single_state_counties[,c('sub_region_2_code',
'date',
selected_symptom)]
# Shape into what we want
colnames(single_state_symptom) = c('geo_value', 'time_value', 'value')
single_state_symptom = single_state_symptom %>% filter (
!is.na(value),
)
single_state_symptom = single_state_symptom %>% transmute (
geo_value = sprintf('%05d', as.numeric(geo_value)),
signal = symptom,
time_value = time_value,
direction = NA,
issue = lubridate::today(),
lag = issue - time_value,
value = value,
stderr = NA,
sample_size = NA,
data_source = 'google_symptoms',
)
}Summary
- 422 symptom signals, each with varying levels of availability. The most sparsely available symptoms are still available at at least XXXX counties daily. As a result, we retain all symptoms for this initial round of analysis.
- First, we assess the correlation between each symptom and cases over time. To do this, we group by (symptom, date), and calculate the rank correlation between search interest and cases (the free variable here being counties). (A previous analysis found that leading / lagging the signal does not improve correlation, which is in line with the intuition that searcher interest should provide a “today” snapshot of COVID prevalence).
- We see that there are a handful of symptoms that are highly correlated with cases, but there is a surprising temporal dependency. Specifically, almost all signals suffer a degradation in correlation from XXXX to
- This is likely due to non-stationarity in both searcher behavior as well as in the dynamics of COVID-spread (which is also highly community dependent).
- The symptoms that achieve the greatest single-day correlation with cases include the usual suspects: anosmia (lack of smell), ageusia (lack of taste), fever, XXXX, XXXX. Concerningly, these high-signal symptoms also suffer from degradation during XXXX, with anosmia bottoming out with a near-zero correlation with cases on XXXX. We need to develop an understanding of why even these correlations degrade. Nonetheless, these symptoms will likely form the bedrock of a new Google Symptoms indicator.
- We also examine the symptoms which maximize the minimum absolute correlation – i.e., the symptoms that are uniformly bounded away from zero (many symptoms that achieve high single-day correlations also have days with very low correlation). These symptoms are not medically linked to the coronavirus, based on my own rudimentary understanding. However, we pose a latent variable that may explain why they achieve consistent negative correlation with cases: more cases in a community discourages non-vital medical appointments, decreasing diagnoses which may spur patients to Google unfamiliar terms like “Erythema chronicum migrans”.
- The hypothesis that an increase in cases reduces search interest in medical terms, especially obscure ones, by reducing non-vital medical visits may be further examined by incorporating Safegraph location data for general medical visits (not to be confused with the Doctor’s Visits indicator, which measures the proportion of doctor’s visits that are related to COVID).
- Second, we assess whether the correlation between search interest in these symptoms and cases is homogeneous in location. To do this, we group by (symptom, county), and calculate the correlation varying time.
- TODO
- Finally, we set up a rudimentary prediction problem, modeled on XXXX, to assess the forecasting ability of this new data alone.
- TODO
Initial ingestion and exploration
if (file.exists('symptom_df.RDS')) {
symptom_df = readRDS('symptom_df.RDS')
symptom_names = unique(symptom_df$signal)
} else {
dir.create('./cache/')
ak = load_state_data('ak')
symptom_cols = colnames(ak)[
str_detect(colnames(ak), 'symptom:')]
symptom_names = str_replace(symptom_cols, fixed('symptom:'), '')
symptom_df_list = vector('list', length(symptom_names))
names(symptom_df_list) = symptom_names
for (symptom in symptom_names) {
cat(symptom, '...\n')
states_list = vector('list', length(states))
for (idx in 1:length(states)) {
state = states[idx]
states_list[[idx]] = pull_data_state(state, symptom)
}
symptom_df_list[[symptom]] = bind_rows(states_list)
}
symptom_df = bind_rows(symptom_df_list)
saveRDS(symptom_df, 'symptom_df.RDS')
}start_day = "2020-03-01"
end_day = "2020-08-15"
df_inum = covidcast_signal(data_source = "jhu-csse",
signal = "confirmed_7dav_incidence_num",
start_day = start_day, end_day = end_day)
case_num = 500
geo_values = df_inum %>% group_by(geo_value) %>%
summarize(total = sum(value)) %>%
filter(total >= case_num) %>% pull(geo_value)
df_inum_act = df_inum %>% filter(geo_value %in% geo_values)symptom_df_act = symptom_df %>% filter (
geo_value %in% geo_values,
)Here we plot the availaibility of each symptom over time (proportion is percentage of counties for which the symptom was available). We see that for each signal, the availability level is consistent over time, subject to a strong weekend effect.
availability_df = symptom_df_act %>% group_by (
time_value,
signal,
) %>% summarize (
prop_available = n() / length(geo_values),
) %>% ungroup (
)## `summarise()` regrouping output by 'time_value' (override with `.groups` argument)plt = (ggplot(availability_df)
+ geom_line(aes(x=time_value,
y=prop_available,
group=factor(signal)),
color='dodgerblue4',
size=0.1)
)
plt
The symptoms for which data is most sparse are:
most_missing = availability_df %>% group_by (
signal,
) %>% summarize (
avg_available = mean(prop_available)
) %>% ungroup (
) %>% filter (
avg_available <= 0.05
) %>% arrange (
avg_available,
)## `summarise()` ungrouping output (override with `.groups` argument)print(most_missing)## # A tibble: 16 x 2
## signal avg_available
## <chr> <dbl>
## 1 Viral pneumonia 0.0279
## 2 Aphonia 0.0334
## 3 Crackles 0.0353
## 4 Burning Chest Pain 0.0363
## 5 Urinary urgency 0.0369
## 6 Polydipsia 0.0371
## 7 Photodermatitis 0.0380
## 8 Dysautonomia 0.0414
## 9 Shallow breathing 0.0414
## 10 Allergic conjunctivitis 0.0435
## 11 Laryngitis 0.0437
## 12 Ventricular fibrillation 0.0455
## 13 Angular cheilitis 0.0462
## 14 Myoclonus 0.0462
## 15 Rectal pain 0.0489
## 16 Stridor 0.0491For the signal that is most sparsely available, the number of counties at which it tends to be available daily is:
print(min(most_missing$avg_available) * length(geo_values))## [1] 29.52653Based on this, we leave all the symptoms in for the full correlations analysis.
Correlations
cor_list = vector('list', length(symptom_names))
names(cor_list) = symptom_names
if (file.exists('cor_df.RDS')) {
cor_df = readRDS('cor_df.RDS')
} else {
for (symptom in symptom_names) {
cat(symptom, '...\n')
df_cor1 = covidcast_cor(symptom_df_act %>% filter(signal == symptom),
df_inum_act,
by = "time_value",
method = "spearman")
df_cor1['signal'] = symptom
cor_list[[symptom]] = df_cor1
}
cor_df = bind_rows(cor_list)
saveRDS(cor_df, 'cor_df.RDS')
}
cor_df = cor_df %>% left_join(
signal_description_df,
on='signal',
)## Joining, by = "signal"Correlation over time: all symptoms
## Warning: Removed 7174 row(s) containing missing values (geom_path).
Correlation over time: largest single-day correlation
When we discuss the “size” of a correlation, we consider the absolute value of correlation.
top_cor_signals = plot_cor_df %>% group_by (
signal,
) %>% filter (
abs(value) == max(abs(value), na.rm=TRUE),
) %>% ungroup (
) %>% arrange(
-abs(value),
) %>% head (
5,
)
top_cor_sum_stats = plot_cor_df %>% filter (
signal %in% top_cor_signals$signal,
) %>% group_by (
signal,
) %>% summarize (
min = min(value, na.rm=TRUE),
quart1 = quantile(value, 0.25, na.rm=TRUE),
med = median(value, na.rm=TRUE),
mean = mean(value, na.rm=TRUE),
quart3 = quantile(value, 0.75, na.rm=TRUE),
max = max(value, na.rm=TRUE),
) %>% ungroup (
)## `summarise()` ungrouping output (override with `.groups` argument)print('Symptoms with the largest all-time correlation:')## [1] "Symptoms with the largest all-time correlation:"print(top_cor_signals %>% left_join(top_cor_sum_stats, on='signal')
%>% select(-time_value, -value),
width=100)## Joining, by = "signal"## # A tibble: 5 x 8
## signal description min quart1
## <chr> <chr> <dbl> <dbl>
## 1 Viral pneumonia <NA> -0.898 -0.269
## 2 Anosmia loss of smell -0.0788 0.403
## 3 Ageusia loss of taste -0.423 0.260
## 4 Erythema chronicum migrans expanding rash early in lyme disease -0.710 -0.556
## 5 Photodermatitis allergic rash that reqs light -0.709 -0.487
## med mean quart3 max
## <dbl> <dbl> <dbl> <dbl>
## 1 0.0853 0.0216 0.327 0.668
## 2 0.610 0.545 0.725 0.833
## 3 0.552 0.450 0.682 0.797
## 4 -0.276 -0.325 -0.179 0.208
## 5 -0.366 -0.349 -0.211 0.184plt = (ggplot(plot_cor_df)
+ geom_line(aes(x=time_value,
y=value,
group=factor(signal)),
data=plot_cor_df %>% filter (
!signal %in% top_cor_signals$signal
),
color='cornsilk',
size=0.1,
alpha=1.0)
+ geom_line(aes(x=time_value,
y=value,
group=factor(signal),
colour=factor(signal)
),
data=plot_cor_df %>% filter (
signal %in% top_cor_signals$signal,
),
#color='darkorange',
size=0.3)
+ ylab('rank correlation')
+ scale_x_date(breaks=lubridate::ymd(c('2020-03-01',
'2020-03-15', '2020-04-01', '2020-04-15', '2020-05-01',
'2020-05-15', '2020-06-01', '2020-06-15', '2020-07-01',
'2020-07-15', '2020-08-01', '2020-08-15')))
+ theme(axis.text.x = element_text(angle = 45))
+ ggtitle("Top 5 signals by all-time max(|corr|)")
)
plt## Warning: Removed 7089 row(s) containing missing values (geom_path).## Warning: Removed 85 row(s) containing missing values (geom_path).
Correlation over time: “consistently away from zero” symptoms
## `summarise()` ungrouping output (override with `.groups` argument)## [1] "Symptoms that consistently stay away from 0 correlation:"## Joining, by = "signal"## # A tibble: 5 x 8
## signal description min quart1
## <chr> <chr> <dbl> <dbl>
## 1 Podalgia pain in the foot -0.423 -0.279
## 2 Restless legs syndrome <NA> -0.528 -0.410
## 3 Anosmia loss of smell -0.0788 0.403
## 4 Purpura red/purple skin spots; 'blood spots' -0.533 -0.366
## 5 Radiculopathy pinched nerve -0.477 -0.349
## med mean quart3 max
## <dbl> <dbl> <dbl> <dbl>
## 1 -0.217 -0.223 -0.166 -0.0710
## 2 -0.337 -0.310 -0.215 -0.0530
## 3 0.610 0.545 0.725 0.833
## 4 -0.283 -0.289 -0.206 -0.0477
## 5 -0.278 -0.256 -0.154 -0.0425## Warning: Removed 7089 row(s) containing missing values (geom_path).## Warning: Removed 85 row(s) containing missing values (geom_path).
## [1] "Podalgia" "Restless legs syndrome" "Anosmia"
## [4] "Purpura" "Radiculopathy"## [1] "Anosmia"Interestingly, although anosmia technically is one of the “most stable” by our criterion, its distribution actually covers zero.
Correlation across location: TODO
if (file.exists('geo_cor_df.RDS')) {
geo_cor_df = readRDS('geo_cor_df.RDS')
} else {
geo_cor_list = vector('list', length(symptom_names))
names(geo_cor_list) = symptom_names
for (symptom in symptom_names) {
cat(symptom, '...\n')
df_cor1 = covidcast_cor(symptom_df_act %>% filter(signal == symptom),
df_inum_act,
by = "geo_value",
method = "spearman")
df_cor1['signal'] = symptom
geo_cor_list[[symptom]] = df_cor1
}
geo_cor_df = bind_rows(geo_cor_list)
saveRDS(geo_cor_df, 'geo_cor_df.RDS')
}
geo_cor_df = geo_cor_df %>% left_join(
signal_description_df,
on='signal',
)## Joining, by = "signal"for (symptom in top_cor_signals$signal) {
df_cor2 = geo_cor_df %>% filter (signal == symptom)
df_cor2$time_value = min_available_time
df_cor2$issue = min_available_time
attributes(df_cor2)$geo_type = 'county'
class(df_cor2) = c("covidcast_signal", "data.frame")
n_available_county = df_cor2 %>% filter (!is.na(value)) %>% nrow()
# Plot choropleth maps, using the covidcast plotting functionality
title_text = sprintf("Correlations between cases and %s (%d counties)",
symptom, n_available_county)
if (!is.na(df_cor2$description[1])) {
title_text = paste0(title_text, '\n', sprintf('(%s)', df_cor2$description[1]))
}
print(plot(df_cor2,
title = title_text,
range = c(-1, 1), choro_col = c("orange","lightblue", "purple")))
}
for (symptom in top_min_cor$signal) {
df_cor2 = geo_cor_df %>% filter (signal == symptom)
df_cor2$time_value = min_available_time
df_cor2$issue = min_available_time
attributes(df_cor2)$geo_type = 'county'
class(df_cor2) = c("covidcast_signal", "data.frame")
n_available_county = df_cor2 %>% filter (!is.na(value)) %>% nrow()
# Plot choropleth maps, using the covidcast plotting functionality
title_text = sprintf("Correlations between cases and %s (%d counties)",
symptom, n_available_county)
if (!is.na(df_cor2$description[1])) {
title_text = paste0(title_text, '\n', sprintf('(%s)', df_cor2$description[1]))
}
print(plot(df_cor2,
title = title_text,
range = c(-1, 1), choro_col = c("orange","lightblue", "purple")))
}
TODO:
- Re-create overall correlations plot
- Look at the “consistently ok” ones - top 5 min
- Table that lists symptom, summary statistics of correlation (transform existing table…….
- Top 5: Group by location, aggregate over time, run correlation
- With county-sliced correlations, produce chloropeth for top 5 layperson description. Then plot using: https://cmu-delphi.github.io/covidcast/covidcastR/articles/correlation-utils.html
-
Run lasso / quantile lasso fit
- One fit that uses my “curated” variables
- One fit that just throws everything in
- Polish and add some prose
Appendix
Some things I’m still worried about / next steps:
- Efficient way to present data? Distribution of correlations over time, and the symptoms that consistently achieved high correlations
- For those very useful symptoms, is the correlation over time homogeneous in geography, or not? If not homogeneous in geography, we may want to be careful with fitting a global model; we don’t want to inadvertently make poor predictions for some places.